Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Generating Image Filters for Target Recognition by Genetic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving Task Specific Image Operator
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
OpenGL(R) Shading Language (2nd Edition)
OpenGL(R) Shading Language (2nd Edition)
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Computer Vision: A Taxonomic Tutorial
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
An Adaptive On-Line Evolutionary Visual System
SASOW '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops
A Real-Time Evolutionary Object Recognition System
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Evolving edge detectors with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Artificial creatures for object tracking and segmentation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Visual Learning by Evolutionary and Coevolutionary Feature Synthesis
IEEE Transactions on Evolutionary Computation
Evolving object detectors with a GPU accelerated vision system
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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Recognizing arbitrary objects in images or video sequences is a difficult task for a computer vision system. We work towards automated learning of object detectors from video sequences (without user interaction). Our system uses object motion as an important cue to detect independently moving objects in the input sequence. The largest object is always taken as the teaching input, i.e. the object to be extracted. We use Cartesian Genetic Programming to evolve image processing routines which deliver the maximum output at the same position where the detected object is located. The graphics processor (GPU) is used to speed up the image processing. Our system is a step towards automated learning of object detectors.